• Publications
  • Influence
A short introduction to probabilistic soft logic
TLDR
This paper provides an overview of the PSL language and its techniques for inference and weight learning. Expand
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Query-driven Active Surveying for Collective Classification
TLDR
We study the problem of query-driven collective classification in an active learning setting. Expand
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'Beating the news' with EMBERS: forecasting civil unrest using open source indicators
TLDR
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Expand
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Joint Models of Disagreement and Stance in Online Debate
TLDR
We introduce a scalable unified probabilistic modeling framework for stance classification models that 1) are collective, 2) reason about disagreement, and 3) can model stance at either the author level or at the post level. Expand
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Beyond Parity: Fairness Objectives for Collaborative Filtering
TLDR
We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Expand
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Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
TLDR
We use probabilistic soft logic (PSL) to model student engagement by capturing domain knowledge about student interactions and performance and demonstrate that modeling engagement is helpful in predicting student performance. Expand
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Hinge-loss Markov Random Fields: Convex Inference for Structured Prediction
TLDR
We use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models with log-concave density functions over continuous variables, which can represent confidences in discrete predictions. Expand
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Learning Latent Engagement Patterns of Students in Online Courses
TLDR
We study the different aspects of online student behavior in MOOCs, develop a large-scale, data-driven approach for modeling student engagement in online courses based on latent representations. Expand
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Semantic Model Vectors for Complex Video Event Recognition
TLDR
We propose semantic model vectors, an intermediate level semantic representation, as a basis for modeling and detecting complex events in unconstrained real-world videos, such as those from YouTube. Expand
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Loopy Belief Propagation for Bipartite Maximum Weight b-Matching
TLDR
We formulate the weighted b-matching objective function as a probability distribution function and prove that belief propagation (BP) on its graphical model converges to the optimum. Expand
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